Planning For Non-Player Characters By Learning From Demonstration

In video games, state of the art non-player character (NPC) behavior generation typically depends on hard-coding NPC actions. In many game situations however, it is hard to foresee how an NPC should behave to appear intelligent or to accommodate human preferences for NPC behavior. We advocate the creation of a more flexible method to allow players (and developers) to train NPCs to execute novel behaviors which are not hard-coded. In particular, we investigate search-based planning approaches using demonstration to guide the search through high-dimensional spaces that represent the full state of the game. To this end, we developed the Training Graph heuristic, an extension of the Experience Graph heuristic, that guides a search smoothly and effectively even when a demonstration is unreachable in the search space, and ensures that more of the demonstrations are utilized to better train the NPC\u27s behavior. To deal with variance in the initial conditions of such planning problems, we have developed heuristics in the Multi-Heuristic A* framework to adapt demonstration trace data to new problems. We evaluate our approach in the Creation Engine game engine by modifying The Elder Scrolls V: Skyrim (Skyrim) to accommodate our NPC behavior generators and experiments. In Skyrim, players are given quests which are composed of several objectives. NPCs in the game sometimes accompany the player on quests, but state-of-the-art companion NPC AI is not sophisticated enough to behave according to arbitrary player desires. We hope that our work will lead to the creation of trainable NPC AI. This will enable novel gameplay mechanics for video game players and may augment video game production by allowing developers to train NPCs instead of hard-coding complex behaviors

Multi-Objective Pathfinding in Dynamic Environments

Traditional pathfinding techniques are known for calculating the shortest path from a given start point to a designated target point on a directed graph. These techniques, however, are inapplicable to pathfinding problems where the shortest path may prove to be hazardous for traversal, or where multiple costs of differing unit-types lie along the same path. Moreover, the shortest path may not be optimal if it requires forfeiting a valuable resource. While strategic methods have been proposed in the past to completely avoid paths determined to be dangerous, these methods lack the functionality to provide agents the ability to decide which resources are more valuable for conservation, and which resources possess the greatest risk at being lost. For environments where risk varies dynamically across edges, we propose a solution that can determine a path of least expected weight based on multiple properties of edges. With this Multi-Objective Pathfinding technique, agents can make decisions influenced by highest priority objectives and their preferences to trading off some resources for others. The solution is based on traditional pathfinding techniques, extending their usability to cover strategic and dynamic scenarios where additional properties contained within the search map could render them useless. Nevertheless, our solution is compatible with problems where the goal is to simply find the least weighted path, otherwise known as the objectively resource-conservative path among a set of vertices in a graph

Development of a stealth game with multi-agent artificial intelligence

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Francesc Dantí[en] Artificial intelligence is used in video games to create believable characters that challenge the player and enrich the playing experience. The stealth genre is one of the types of games where it is fundamental to have non-player characters with the ability to perceive the environment, detect some of the player character’s actions and react to them with astute strategies. The goal of this project was to develop a stealth game with enemy characters that would cooperate to defeat the player. In the final game, the enemies patrol the game’s world trying to locate the player character, and they investigate any potential signs of player actions in the environment, such as objects in motion or bodies of unconscious characters on the floor. The findings of an enemy are immediately communicated to the rest of agents, and the player character is chased and attacked by the enemies after being spotted. While some of the agents pursue the player character and use close combat moves, others reduce speed to shoot the character in the head with precision from distant positions. The game was developed with the Unreal Engine 4 game engine. The reasoning and decisionmaking of enemies was modelled using behaviour trees. The implemented system makes agents capable of perceiving visual and auditory stimuli and communicating information to other agents, who compare it with their own knowledge to take independent decisions. The whole set of enemies form an artificial intelligence-based multi-agent system and cooperate to achieve the shared goal of defeating the player in the shortest time possible. The ability of the numerous enemies to spot the player character and detect sounds penalizes noisy and reckless players, who are rapidly defeated. Unlike them, those players who struggle to be silent and elusive have a reasonable chance to win the game, approaching enemies from behind, knocking them out stealthily, and hiding the bodies to avoid leaving traces that other agents would try to use to find the player character. Stones and other objects can be thrown to make noises and distract the enemies. Nevertheless, the agents’ ability to locate the player character should not be underestimated: no hidden location is permanently safe

Ground collision avoidance systems on United States Air Force aircraft

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 1995.Includes bibliographical references (p. 91-92).This thesis examines a specific type of USAF aircraft mishaps - Controlled Flight Into Terrain (CFIT) mishaps. The thesis presents data on CFIT mishaps, causes, and efforts to reduce CFIT mishaps through the development and adoption of Ground Collision Avoidance Systems (GCAS) or similar designs - Ground Proximity Warning Systems (GPWS). GPWS exist today on some USAF aircraft, but many times these systems are inadequate (as evidenced by the continued occurrence of CFIT mishaps). Both ongoing and future initiatives by the USAF to adopt and develop better GPWS/GCAS systems were studied. An analysis was performed which studied the cost to the USAF (and the U.S. taxpayer) as a result of CFIT mishaps, and compared with an analysis of the cost to develop and implement improved GPWS/GCAS systems. The results show conclusively that installing GCAS/GPWS on a majority of USAF aircraft is cost effective. Technology exist today which could improve existing GPWS performance, and although efforts to improve GPWS are moving forward, some resistance does exist. Possible reasons for resistance of GCAS/GPWS adoption were studied and several recommendations were made on how to improve the adoption of these systems within the USAF.by Jan W. Scofield.S.M

Aeronautics Research and Technology Program and specific objectives, fiscal year 1982

The Aeronautics Research and Technology program is broken down into two program areas (research and technology base, and systems technology programs) which are further broken down into succeedingly more detailed activities to form a work breakdown structure for the aeronautics program: program area, program/discipline objective, specific objective, and research and technology objective and plan (RTOP). A detailed view of this work breakdown structure down to the specific objective level is provided, and goals or objectives at each of these levels are set forth. What is to be accomplished and why are addressed, but not how. The letter falls within the domain of the RTOP

Aeronautics research and technology program and specific objectives

Aeronautics research and technology program objectives in fluid and thermal physics, materials and structures, controls and guidance, human factors, multidisciplinary activities, computer science and applications, propulsion, rotorcraft, high speed aircraft, subsonic aircraft, and rotorcraft and high speed aircraft systems technology are addressed

Engineering Technology Reports, Volume 1: Laboratory Directed Research and Development FY00

In FY-2000, Engineering at Lawrence Livermore National Laboratory faced significant pressures to meet critical project milestones, and immediate demands to facilitate the reassignment of employees as the National Ignition Facility (the 600-TW laser facility being designed and built at Livermore, and one of the largest R&D construction projects in the world) was in the process of re-baselining its plan while executing full-speed its technology development efforts. This drive for change occurred as an unprecedented level of management and program changes were occurring within LLNL. I am pleased to report that we met many key milestones and achieved numerous technological breakthroughs. This report summarizes our efforts to perform feasibility and reduce-to-practice studies, demonstrations, and/or techniques--as structured through our technology centers. Whether using computational engineering to predict how giant structures like suspension bridges will respond to massive earthquakes or devising a suitcase-sized microtool to detect chemical and biological agents used by terrorists, we have made solid technical progress. Five Centers focus and guide longer-term investments within Engineering, as well as impact all of LLNL. Each Center is responsible for the vitality and growth of the core technologies it represents. My goal is that each Center will be recognized on an international scale for solving compelling national problems requiring breakthrough innovation. The Centers and their leaders are as follows: Center for Complex Distributed Systems--David B. McCallen; Center for Computational Engineering--Kyran D. Mish; Center for Microtechnology--Raymond P. Mariella, Jr.; Center for Nondestructive Characterization--Harry E. Martz, Jr.; and Center for Precision Engineering--Keith Carlisle

Aeronautical engineering: A continuing bibliography with indexes (supplement 267)

This bibliography lists 661 reports, articles, and other documents introduced into the NASA scientific and technical information system in June, 1991. Subject coverage includes design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; theoretical and applied aspects of aerodynamics and general fluid dynamics; electrical engineering; aircraft control; remote sensing; computer sciences; nuclear physics; and social sciences

The Complete Reference (Volume 4)

This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems. The book is divided into four parts: Part 1 features two papers on navigation, discussing SLAM and path planning. Part 2 focuses on the integration of ROS into quadcopters and their control. Part 3 then discusses two emerging applications for robotics: cloud robotics, and video stabilization. Part 4 presents tools developed for ROS; the first is a practical alternative to the roslaunch system, and the second is related to penetration testing. This book is a valuable resource for ROS users and wanting to learn more about ROS capabilities and features.info:eu-repo/semantics/publishedVersio